Overview

Dataset statistics

Number of variables9
Number of observations52416
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.8 MiB
Average record size in memory135.5 B

Variable types

DateTime1
Numeric8

Alerts

Zone 1 Power Consumption is highly overall correlated with Zone 2 Power Consumption and 1 other fieldsHigh correlation
Zone 2 Power Consumption is highly overall correlated with Zone 1 Power Consumption and 1 other fieldsHigh correlation
Zone 3 Power Consumption is highly overall correlated with Zone 1 Power Consumption and 1 other fieldsHigh correlation
diffuse flows is highly overall correlated with general diffuse flowsHigh correlation
general diffuse flows is highly overall correlated with diffuse flowsHigh correlation
DateTime has unique valuesUnique

Reproduction

Analysis started2024-04-12 01:55:56.860769
Analysis finished2024-04-12 01:56:05.317882
Duration8.46 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

DateTime
Date

UNIQUE 

Distinct52416
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size409.6 KiB
Minimum2017-01-01 00:00:00
Maximum2017-12-30 23:50:00
2024-04-12T11:56:05.357179image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:05.421850image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Temperature
Real number (ℝ)

Distinct3437
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.810024
Minimum3.247
Maximum40.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size409.6 KiB
2024-04-12T11:56:05.489856image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3.247
5-th percentile9.8
Q114.41
median18.78
Q322.89
95-th percentile28.39
Maximum40.01
Range36.763
Interquartile range (IQR)8.48

Descriptive statistics

Standard deviation5.8154758
Coefficient of variation (CV)0.30916898
Kurtosis-0.30332122
Mean18.810024
Median Absolute Deviation (MAD)4.26
Skewness0.19671914
Sum985946.22
Variance33.819759
MonotonicityNot monotonic
2024-04-12T11:56:05.557705image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.18 58
 
0.1%
20.76 56
 
0.1%
19.79 55
 
0.1%
20.74 52
 
0.1%
20.83 51
 
0.1%
15.85 51
 
0.1%
15.84 51
 
0.1%
21 50
 
0.1%
20.89 50
 
0.1%
20.37 49
 
0.1%
Other values (3427) 51893
99.0%
ValueCountFrequency (%)
3.247 1
< 0.1%
3.441 1
< 0.1%
3.541 1
< 0.1%
3.555 1
< 0.1%
3.582 1
< 0.1%
3.629 1
< 0.1%
3.638 1
< 0.1%
3.662 1
< 0.1%
3.681 1
< 0.1%
3.706 1
< 0.1%
ValueCountFrequency (%)
40.01 1
< 0.1%
39.78 1
< 0.1%
39.76 1
< 0.1%
39.74 1
< 0.1%
39.73 1
< 0.1%
39.7 1
< 0.1%
39.67 1
< 0.1%
39.6 1
< 0.1%
39.59 1
< 0.1%
39.55 1
< 0.1%

Humidity
Real number (ℝ)

Distinct4443
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.259518
Minimum11.34
Maximum94.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size409.6 KiB
2024-04-12T11:56:05.623603image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum11.34
5-th percentile39.45
Q158.31
median69.86
Q381.4
95-th percentile88.9
Maximum94.8
Range83.46
Interquartile range (IQR)23.09

Descriptive statistics

Standard deviation15.551177
Coefficient of variation (CV)0.2278243
Kurtosis-0.12185965
Mean68.259518
Median Absolute Deviation (MAD)11.54
Skewness-0.62516601
Sum3577890.9
Variance241.83911
MonotonicityNot monotonic
2024-04-12T11:56:05.685796image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85.9 197
 
0.4%
84.6 190
 
0.4%
85 189
 
0.4%
86.6 187
 
0.4%
86.3 186
 
0.4%
85.8 185
 
0.4%
87.2 175
 
0.3%
86.8 173
 
0.3%
87.4 171
 
0.3%
86.9 171
 
0.3%
Other values (4433) 50592
96.5%
ValueCountFrequency (%)
11.34 2
< 0.1%
11.57 1
< 0.1%
11.94 1
< 0.1%
12.27 1
< 0.1%
12.3 1
< 0.1%
12.6 1
< 0.1%
12.74 1
< 0.1%
12.87 1
< 0.1%
13.04 1
< 0.1%
13.07 1
< 0.1%
ValueCountFrequency (%)
94.8 3
 
< 0.1%
94.7 4
 
< 0.1%
94.6 1
 
< 0.1%
94.5 2
 
< 0.1%
94.4 1
 
< 0.1%
94.3 2
 
< 0.1%
94.2 4
 
< 0.1%
94.1 6
< 0.1%
94 9
< 0.1%
93.9 10
< 0.1%

Wind Speed
Real number (ℝ)

Distinct548
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9594889
Minimum0.05
Maximum6.483
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size409.6 KiB
2024-04-12T11:56:05.746177image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.069
Q10.078
median0.086
Q34.915
95-th percentile4.923
Maximum6.483
Range6.433
Interquartile range (IQR)4.837

Descriptive statistics

Standard deviation2.348862
Coefficient of variation (CV)1.1987116
Kurtosis-1.7831692
Mean1.9594889
Median Absolute Deviation (MAD)0.016
Skewness0.46242332
Sum102708.57
Variance5.5171525
MonotonicityNot monotonic
2024-04-12T11:56:05.812562image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.082 2291
 
4.4%
0.083 1979
 
3.8%
0.084 1831
 
3.5%
0.081 1804
 
3.4%
0.085 1513
 
2.9%
0.08 1502
 
2.9%
0.073 1485
 
2.8%
4.919 1430
 
2.7%
4.916 1375
 
2.6%
0.072 1369
 
2.6%
Other values (538) 35837
68.4%
ValueCountFrequency (%)
0.05 1
 
< 0.1%
0.053 5
 
< 0.1%
0.054 10
< 0.1%
0.055 13
< 0.1%
0.056 9
< 0.1%
0.057 17
< 0.1%
0.058 4
 
< 0.1%
0.059 7
< 0.1%
0.06 9
< 0.1%
0.061 11
< 0.1%
ValueCountFrequency (%)
6.483 1
< 0.1%
6.325 1
< 0.1%
6.2 1
< 0.1%
5.817 1
< 0.1%
5.69 1
< 0.1%
5.402 1
< 0.1%
5.375 1
< 0.1%
5.044 1
< 0.1%
5.019 1
< 0.1%
5.014 2
< 0.1%

general diffuse flows
Real number (ℝ)

HIGH CORRELATION 

Distinct10504
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182.69661
Minimum0.004
Maximum1163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size409.6 KiB
2024-04-12T11:56:05.877285image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.004
5-th percentile0.037
Q10.062
median5.0355
Q3319.6
95-th percentile782
Maximum1163
Range1162.996
Interquartile range (IQR)319.538

Descriptive statistics

Standard deviation264.40096
Coefficient of variation (CV)1.4472132
Kurtosis0.40276752
Mean182.69661
Median Absolute Deviation (MAD)5.0095
Skewness1.3069729
Sum9576225.7
Variance69907.867
MonotonicityNot monotonic
2024-04-12T11:56:05.943731image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.055 1576
 
3.0%
0.062 1557
 
3.0%
0.051 1497
 
2.9%
0.059 1474
 
2.8%
0.066 1459
 
2.8%
0.048 1388
 
2.6%
0.044 1292
 
2.5%
0.073 1262
 
2.4%
0.04 1125
 
2.1%
0.077 1116
 
2.1%
Other values (10494) 38670
73.8%
ValueCountFrequency (%)
0.004 3
 
< 0.1%
0.007 9
 
< 0.1%
0.011 38
 
0.1%
0.015 97
 
0.2%
0.018 184
 
0.4%
0.022 309
 
0.6%
0.026 436
0.8%
0.029 566
1.1%
0.033 699
1.3%
0.037 924
1.8%
ValueCountFrequency (%)
1163 1
< 0.1%
1122 1
< 0.1%
1102 1
< 0.1%
1099 1
< 0.1%
1082 1
< 0.1%
1069 1
< 0.1%
1055 1
< 0.1%
1051 1
< 0.1%
1050 1
< 0.1%
1044 1
< 0.1%

diffuse flows
Real number (ℝ)

HIGH CORRELATION 

Distinct10449
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.028022
Minimum0.011
Maximum936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size409.6 KiB
2024-04-12T11:56:06.008863image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.011
5-th percentile0.085
Q10.122
median4.456
Q3101
95-th percentile331.85
Maximum936
Range935.989
Interquartile range (IQR)100.878

Descriptive statistics

Standard deviation124.21095
Coefficient of variation (CV)1.6555274
Kurtosis7.0029015
Mean75.028022
Median Absolute Deviation (MAD)4.393
Skewness2.4569065
Sum3932668.8
Variance15428.36
MonotonicityNot monotonic
2024-04-12T11:56:06.074261image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.115 1260
 
2.4%
0.122 1218
 
2.3%
0.119 1201
 
2.3%
0.126 1150
 
2.2%
0.111 1140
 
2.2%
0.13 1093
 
2.1%
0.104 1085
 
2.1%
0.1 963
 
1.8%
0.137 926
 
1.8%
0.096 915
 
1.7%
Other values (10439) 41465
79.1%
ValueCountFrequency (%)
0.011 1
 
< 0.1%
0.019 3
 
< 0.1%
0.022 2
 
< 0.1%
0.026 4
 
< 0.1%
0.03 10
 
< 0.1%
0.033 18
 
< 0.1%
0.037 20
 
< 0.1%
0.041 34
0.1%
0.044 53
0.1%
0.048 74
0.1%
ValueCountFrequency (%)
936 1
< 0.1%
933 1
< 0.1%
922 2
< 0.1%
909 1
< 0.1%
903 1
< 0.1%
897 1
< 0.1%
863 1
< 0.1%
856 1
< 0.1%
855 1
< 0.1%
851 1
< 0.1%

Zone 1 Power Consumption
Real number (ℝ)

HIGH CORRELATION 

Distinct27709
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32344.971
Minimum13895.696
Maximum52204.395
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size409.6 KiB
2024-04-12T11:56:06.189882image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum13895.696
5-th percentile21867.342
Q126310.669
median32265.92
Q337309.018
95-th percentile44712.058
Maximum52204.395
Range38308.699
Interquartile range (IQR)10998.349

Descriptive statistics

Standard deviation7130.5626
Coefficient of variation (CV)0.22045352
Kurtosis-0.75405439
Mean32344.971
Median Absolute Deviation (MAD)5513.8039
Skewness0.22886369
Sum1.695394 × 109
Variance50844922
MonotonicityNot monotonic
2024-04-12T11:56:06.256042image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34560 30
 
0.1%
23040 24
 
< 0.1%
28800 19
 
< 0.1%
25920 18
 
< 0.1%
23672.42196 13
 
< 0.1%
35441.31148 13
 
< 0.1%
21063.34426 12
 
< 0.1%
21950.76923 12
 
< 0.1%
31680 12
 
< 0.1%
22880.68085 11
 
< 0.1%
Other values (27699) 52252
99.7%
ValueCountFrequency (%)
13895.6962 1
< 0.1%
13920 1
< 0.1%
13932.1519 1
< 0.1%
14090.12658 1
< 0.1%
14327.08861 1
< 0.1%
14557.97468 1
< 0.1%
14612.65823 1
< 0.1%
15013.67089 1
< 0.1%
15524.05063 1
< 0.1%
15572.65823 1
< 0.1%
ValueCountFrequency (%)
52204.39512 1
< 0.1%
52146.85905 1
< 0.1%
52038.1798 1
< 0.1%
51955.07214 1
< 0.1%
51916.71476 1
< 0.1%
51820.82131 1
< 0.1%
51776.07103 1
< 0.1%
51737.71365 1
< 0.1%
51731.32075 1
< 0.1%
51718.53496 1
< 0.1%

Zone 2 Power Consumption
Real number (ℝ)

HIGH CORRELATION 

Distinct29621
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21042.509
Minimum8560.0815
Maximum37408.861
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size409.6 KiB
2024-04-12T11:56:06.320471image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum8560.0815
5-th percentile13284.146
Q116980.766
median20823.168
Q324713.718
95-th percentile30387.137
Maximum37408.861
Range28848.779
Interquartile range (IQR)7732.9515

Descriptive statistics

Standard deviation5201.4659
Coefficient of variation (CV)0.24718848
Kurtosis-0.43739724
Mean21042.509
Median Absolute Deviation (MAD)3867.0469
Skewness0.32887602
Sum1.1029642 × 109
Variance27055247
MonotonicityNot monotonic
2024-04-12T11:56:06.383304image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21600 16
 
< 0.1%
25200 12
 
< 0.1%
14148.32827 11
 
< 0.1%
23400 11
 
< 0.1%
22800 11
 
< 0.1%
16158.83576 11
 
< 0.1%
13732.5228 10
 
< 0.1%
18000 10
 
< 0.1%
13539.20973 10
 
< 0.1%
22962.16216 10
 
< 0.1%
Other values (29611) 52304
99.8%
ValueCountFrequency (%)
8560.081466 1
< 0.1%
8585.743381 1
< 0.1%
8633.401222 1
< 0.1%
8651.731161 1
< 0.1%
8787.372709 1
< 0.1%
8897.352342 1
< 0.1%
8912.016293 1
< 0.1%
9131.97556 1
< 0.1%
9307.942974 1
< 0.1%
9365.944272 1
< 0.1%
ValueCountFrequency (%)
37408.86076 1
< 0.1%
36645.56962 1
< 0.1%
36482.78775 1
< 0.1%
36437.17001 1
< 0.1%
36429.56705 1
< 0.1%
36391.55227 1
< 0.1%
36353.53749 1
< 0.1%
36201.47835 1
< 0.1%
36129.25026 1
< 0.1%
36110.24287 1
< 0.1%

Zone 3 Power Consumption
Real number (ℝ)

HIGH CORRELATION 

Distinct22838
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17835.406
Minimum5935.1741
Maximum47598.326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size409.6 KiB
2024-04-12T11:56:06.446371image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum5935.1741
5-th percentile9519.3277
Q113129.327
median16415.117
Q321624.1
95-th percentile29905.709
Maximum47598.326
Range41663.152
Interquartile range (IQR)8494.7738

Descriptive statistics

Standard deviation6622.1651
Coefficient of variation (CV)0.3712932
Kurtosis1.0863933
Mean17835.406
Median Absolute Deviation (MAD)3866.1593
Skewness1.0238715
Sum9.3486065 × 108
Variance43853071
MonotonicityNot monotonic
2024-04-12T11:56:06.508302image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17280 26
 
< 0.1%
11520 19
 
< 0.1%
9600 17
 
< 0.1%
9450.180072 17
 
< 0.1%
9588.47539 16
 
< 0.1%
9173.589436 15
 
< 0.1%
9795.918367 15
 
< 0.1%
16366.26506 15
 
< 0.1%
9617.286915 15
 
< 0.1%
9329.171669 14
 
< 0.1%
Other values (22828) 52247
99.7%
ValueCountFrequency (%)
5935.17407 1
< 0.1%
6044.657863 1
< 0.1%
6061.944778 1
< 0.1%
6108.043217 1
< 0.1%
6119.567827 1
< 0.1%
6182.953181 1
< 0.1%
6200.240096 1
< 0.1%
6211.764706 1
< 0.1%
6223.289316 1
< 0.1%
6252.10084 1
< 0.1%
ValueCountFrequency (%)
47598.32636 2
< 0.1%
47580.25105 1
< 0.1%
47507.94979 1
< 0.1%
47441.67364 1
< 0.1%
47435.64854 1
< 0.1%
47429.62343 1
< 0.1%
47405.52301 1
< 0.1%
47291.04603 1
< 0.1%
47278.99582 1
< 0.1%
47266.94561 1
< 0.1%

Interactions

2024-04-12T11:56:04.706416image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:01.882087image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:02.361983image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:02.725743image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:03.112872image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:03.551181image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:03.920687image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:04.322005image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:04.757297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:01.968270image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:02.410456image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:02.779352image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:03.164198image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:03.600904image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:03.973135image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:04.372959image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:04.798223image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:02.031351image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:02.450660image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:02.825090image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:03.207552image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:03.642782image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:04.019436image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:04.416706image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:04.845180image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:02.104151image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:02.497017image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:02.874820image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:03.258494image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:03.689820image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:04.070613image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:04.467514image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:04.892834image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:02.168027image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:02.544074image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:02.922879image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:03.306757image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:03.735773image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:04.122696image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:04.516464image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:04.938297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:02.214799image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:02.587432image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:02.967645image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:03.352910image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:03.779845image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:04.170711image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:04.562977image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:04.987693image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:02.266388image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:02.636796image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:03.020451image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:03.404622image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:03.831427image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:04.223830image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:04.613280image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:05.033410image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:02.316900image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:02.684138image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:03.068997image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:03.453969image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:03.878260image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:04.275629image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-04-12T11:56:04.660420image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2024-04-12T11:56:06.552929image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
HumidityTemperatureWind SpeedZone 1 Power ConsumptionZone 2 Power ConsumptionZone 3 Power Consumptiondiffuse flowsgeneral diffuse flows
Humidity1.000-0.378-0.181-0.300-0.309-0.212-0.253-0.459
Temperature-0.3781.0000.3260.4330.3790.4360.2610.472
Wind Speed-0.1810.3261.0000.1070.0870.0790.0220.157
Zone 1 Power Consumption-0.3000.4330.1071.0000.8510.7480.1250.261
Zone 2 Power Consumption-0.3090.3790.0870.8511.0000.5380.0970.242
Zone 3 Power Consumption-0.2120.4360.0790.7480.5381.000-0.0210.085
diffuse flows-0.2530.2610.0220.1250.097-0.0211.0000.786
general diffuse flows-0.4590.4720.1570.2610.2420.0850.7861.000

Missing values

2024-04-12T11:56:05.093155image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-12T11:56:05.234252image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DateTimeTemperatureHumidityWind Speedgeneral diffuse flowsdiffuse flowsZone 1 Power ConsumptionZone 2 Power ConsumptionZone 3 Power Consumption
01/1/2017 0:006.55973.80.0830.0510.11934055.6962016128.8753820240.96386
11/1/2017 0:106.41474.50.0830.0700.08529814.6835419375.0759920131.08434
21/1/2017 0:206.31374.50.0800.0620.10029128.1012719006.6869319668.43373
31/1/2017 0:306.12175.00.0830.0910.09628228.8607618361.0942218899.27711
41/1/2017 0:405.92175.70.0810.0480.08527335.6962017872.3404318442.40964
51/1/2017 0:505.85376.90.0810.0590.10826624.8101317416.4133718130.12048
61/1/2017 1:005.64177.70.0800.0480.09625998.9873416993.3130717945.06024
71/1/2017 1:105.49678.20.0850.0550.09325446.0759516661.3981817459.27711
81/1/2017 1:205.67878.10.0810.0660.14124777.7215216227.3556217025.54217
91/1/2017 1:305.49177.30.0820.0620.11124279.4936715939.2097316794.21687
DateTimeTemperatureHumidityWind Speedgeneral diffuse flowsdiffuse flowsZone 1 Power ConsumptionZone 2 Power ConsumptionZone 3 Power Consumption
5240612/30/2017 22:207.65070.10.0810.0620.12234323.9543728676.2810715684.99400
5240712/30/2017 22:307.48071.00.0850.0620.10433776.4258628230.7456315546.69868
5240812/30/2017 22:407.39071.20.0790.0660.10033387.0722427814.6670815396.87875
5240912/30/2017 22:507.34071.00.0840.0370.11932815.2091327564.2835215172.14886
5241012/30/2017 23:007.07072.50.0800.0590.09332158.1749027273.3967514987.75510
5241112/30/2017 23:107.01072.40.0800.0400.09631160.4562726857.3182014780.31212
5241212/30/2017 23:206.94772.60.0820.0510.09330430.4182526124.5780914428.81152
5241312/30/2017 23:306.90072.80.0860.0840.07429590.8745225277.6925413806.48259
5241412/30/2017 23:406.75873.00.0800.0660.08928958.1749024692.2368813512.60504
5241512/30/2017 23:506.58074.10.0810.0620.11128349.8098924055.2316713345.49820